4.6 Article

Application of PZT Ceramic Sensors for Composite Structure Monitoring Using Harmonic Excitation Signals and Bayesian Classification Approach

Journal

MATERIALS
Volume 14, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/ma14195468

Keywords

Structural Health Monitoring; impact damage detection; composite structure monitoring; Bayesian classification; PZT transducers applications

Funding

  1. National Center for Research and Development in Poland [LIDER/3/0005/L-9/17/NCBR/2018]

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This paper explores the use of harmonic excitation of PZT sensors for detecting impact damage in composite structures, analyzing the repeatability of damage indication and the possibility of different localization of sensing paths. Furthermore, a new Bayesian approach for data classification is introduced, showing lower false-positive indications compared to the nearest-neighbor algorithm.
The capabilities of ceramic PZT transducers, allowing for elastic wave excitation in a broad frequency spectrum, made them particularly suitable for the Structural Health Monitoring field. In this paper, the approach to detecting impact damage in composite structures based on harmonic excitation of PZT sensor in the so-called pitch-catch PZT network setup is studied. In particular, the repeatability of damage indication for similar configuration of two independent PZT networks is analyzed, and the possibility of damage indication for different localization of sensing paths between pairs of PZT sensors with respect to damage locations is investigated. The approach allowed for differentiation between paths sensitive to the transmission mode of elastic wave interaction and sensitive reflection mode. In addition, a new universal Bayesian approach to SHM data classification is provided in the paper. The defined Bayesian classifier is based on asymptotic properties of Maximum Likelihood estimators and Principal Component Analysis for orthogonal data transformation. Properties of the defined algorithm are compared to the standard nearest-neighbor classifier based on the acquired experimental data. It was shown in the paper that the proposed approach is characterized by lower false-positive indications in comparison with the nearest-neighbor algorithm.

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